Tech Guides - Artificial Intelligence

According to Gartner analyst Dave Cappuccio, 80% of enterprises will have shut down their traditional data centers by 2025, compared to just 10% today. The figures are fitting considering the host of problems faced by traditional data centers. The solution to all these problems lies right in front of us- Incorporating Intelligence in traditional data centers. To support this claim, Gartner also predicts that by 2020, more than 30 percent of data centers that fail to implement AI and Machine Learning will cease to be operationally and economically viable. Across the globe, Data science and AI are influencing the design and development of the modern data centers. With the surge in the amount of data everyday, traditional data centers will eventually get slow and result in an inefficient output. Utilizing AI in ingenious ways, data center operators can drive efficiencies up and costs down. A fitting example of this is the tier-two automated control system implemented at Google to cool its data centers autonomously. The system makes all the cooling-plant tweaks on its own, continuously, in real-time- thus saving up to 30% of the plant’s energy annually. Source: DataCenter Knowledge AI has enabled data center operators to add more workloads on the same physical silicon architecture. They can aggregate and analyze data quickly and generate productive outputs, which is specifically beneficial to companies that deal with immense amounts of data like hospitals, genomic systems, airports, and media companies. How is AI facilitating data centers Let's look at some of the ways that Intelligent data centers will serve as a solution to issues faced by traditionally operated data centers. #1 Energy Efficiency The Delta Airlines data center outage in 2016, that was attributed to electrical-system failure over a three day period, cost the airlines around $150 million, grounding about 2,000 flights. This situation could have been easily averted had the data centers used Machine Learning for their workings. As data centers get ever bigger, more complex and increasingly connected to the cloud, artificial intelligence is becoming an essential tool for keeping things from overheating and saving power at the same time. According to the Energy Department’s U.S. Data Center Energy Usage Report, the power usage of data centers in the United States has grown at about a 4 percent rate annually since 2010 and is expected to hit 73 billion kilowatt-hours by 2020, more than 1.8 percent of the country’s total electricity use. Data centers also contribute about 2 percent of the world’s greenhouse gas emissions, AI techniques can do a lot to make processes more efficient, more secure and less expensive. One of the keys to better efficiency is keeping things cool, a necessity in any area of computing. Google and DeepMind (Alphabet Inc.’s AI division)use of AI to directly control its data center has reduced energy use for cooling by about 30 percent. #2 Server optimization Data centers have to maintain physical servers and storage equipment. AI-based predictive analysis can help data centers distribute workloads across the many servers in the firm. Data center loads can become more predictable and more easily manageable. Latest load balancing tools with built-in AI capabilities are able to learn from past data and run load distribution more efficiently. Companies will be able to better track server performance, disk utilization, and network congestions. Optimizing server storage systems, finding possible fault points in the system, improve processing times and reducing risk factors will become faster. These will in turn facilitate maximum possible server optimization. #3 Failure prediction / troubleshooting Unplanned downtime in a datacenter can lead to money loss. Datacenter operators need to quickly identify the root case of the failure, so they can prioritize troubleshooting and get the datacenter up and running before any data loss or business impact take place. Self managing datacenters make use of AI based deep learning (DL) applications to predict failures ahead of time. Using ML based recommendation systems, appropriate fixes can be inferred upon the system in time. Take for instance the HPE artificial intelligence predictive engine that identifies and solves trouble in the data center. Signatures are built to identify other users that might be affected. Rules are then developed to instigate a solution, which can be automated. The AI-machine learning solution, can quickly interject through the entire system and stop others from inheriting the same issue. #4 Intelligent Monitoring and storing of Data Incorporating machine learning, AI can take over the mundane job of monitoring huge amounts of data and make IT professionals more efficient in terms of the quality of tasks they handle. Litbit has developed the first AI-powered, data center operator, Dac. It uses a human-to-machine learning interface that combines existing human employee knowledge with real-time data. Incorporating over 11,000 pieces of innate knowledge, Dac has the potential to hear when a machine is close to failing, feel vibration patterns that are bad for HDD I/O, and spot intruders. Dac is proof of how AI can help monitor networks efficiently. Along with monitoring of data, it is also necessary to be able to store vast amounts of data securely. AI holds the potential to make more intelligent decisions on - storage optimization or tiering. This will help transform storage management by learning IO patterns and data lifecycles, helping storage solutions etc. Mixed views on the future of AI in data centers? Let’s face the truth, the complexity that comes with huge masses of data is often difficult to handle. Humans ar not as scalable as an automated solution to handle data with precision and efficiency. Take Cisco’s M5 Unified Computing or HPE’s InfoSight as examples. They are trying to alleviate the fact that humans are increasingly unable to deal with the complexity of a modern data center. One of the consequences of using automated systems is that there is always a possibility of humans losing their jobs and being replaced by machines at varying degrees depending on the nature of job roles. AI is predicted to open its doors to robots and automated machines that will soon perform repetitive tasks in the datacenters. On the bright side, organizations could allow employees, freed from repetitive and mundane tasks, to invest their time in more productive, and creative aspects of running a data center. In addition to new jobs, the capital involved in setting up and maintaining a data center is huge. Now add AI to the Datacenter and you have to invest double or maybe triple the amount of money to keep everything running smoothly. Managing and storing all of the operational log data for analysis also comes with its own set of issues. The log data that acts as the input to these ML systems becomes a larger data set than the application data itself. Hence firms need a proper plan in place to manage all of this data. Embracing AI in data centers would mean greater financial benefits from the outset while attracting more customers. It would be interesting to see the turnout of tech companies following Google’s footsteps and implementing AI in their data centers. Tech companies should definitely watch this space to take their data center operations up a notch. 5 ways artificial intelligence is upgrading software engineering Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018 15 millions jobs in Britain at stake with Artificial Intelligence robots set to replace humans at workforce

For readers who are new to deep learning and who might be wondering what a GPU is, let’s start there. To make it simple, consider deep learning as nothing more than a set of calculations - complex calculations, yes, but calculations nonetheless. To run these calculations, you need hardware. Ordinarily, you might just use a normal processor like the CPU inside your laptop. However, this isn’t powerful enough to process at the speed at which deep learning computations need to happen. GPUs, however, can. This is because while a conventional CPU has only a few complex cores, a GPU can have thousands of simple cores. With a GPU, training a deep learning data set can take just hours instead of days. However, although it’s clear that GPUs have significant advantages over CPUs, there is nevertheless a range of GPUs available, each having their own individual differences. Selecting one is ultimately a matter of knowing what your needs are. Let’s dig deeper and find out how to go about shopping for GPUs… What to look for before choosing a GPU? There are a few specifications to consider before picking a GPU. Memory bandwidth: This determines the capacity of a GPU to handle large amounts of data. It is the most important performance metric, as with faster memory bandwidth more data can be processed at higher speeds. Number of cores: This indicates how fast a GPU can process data. A large number of CUDA cores can handle large datasets well. CUDA cores are parallel processors similar to cores in a CPU but their number is in thousands and are not suited for complex calculations that a CPU core can perform. Memory size: For computer vision projects, it is crucial for memory size to be as much as you can afford. But with natural language processing, memory size does not play such an important role. Our pick of GPU devices to choose from The go to choice here is NVIDIA; they have standard libraries that make it simple to set things up. Other graphics cards are not very friendly in terms of the libraries supported for deep learning. NVIDIA CUDA Deep Neural Network library also has a good development community. “Is NVIDIA Unstoppable In AI?” -Forbes “Nvidia beats forecasts as sales of graphics chips for AI keep booming” -SiliconANGLE AMD GPUs are powerful too but lack library support to get things running smoothly. It would be really nice to see some AMD libraries being developed to break the monopoly and give more options to the consumers. NVIDIA RTX 2080 Ti: The RTX line of GPUs are to be released in September 2018. The RTX 2080 Ti will be twice as fast as the 1080 Ti. Price listed on NVIDIA website for founder’s edition is $1,199. RAM: 11 GB Memory bandwidth: 616 GBs/second Cores: 4352 cores @ 1545 MHz NVIDIA RTX 2080: This is more cost efficient than the 2080 Ti at a listed price of $799 on NVIDIA website for the founder’s edition. RAM: 8 GB Memory bandwidth: 448 GBs/second Cores: 2944 cores @ 1710 MHz NVIDIA RTX 2070: This is more cost efficient than the 2080 Ti at a listed price of $599 on NVIDIA website. Note that the other versions of the RTX cards will likely be cheaper than the founder’s edition around a $100 difference. RAM: 8 GB Memory bandwidth: 448 GBs/second Cores: 2304 cores @ 1620 MHz NVIDIA GTX 1080 Ti: Priced at $650 on Amazon. This is a higher end option but offers great value for money, and can also do well in Kaggle competitions. If you need more memory but cannot afford the RTX 2080 Ti go for this. RAM: 11 GB Memory bandwidth: 484 GBs/second Cores: 3584 cores @ 1582 MHz NVIDIA GTX 1080: Priced at $584 on Amazon. This is a mid-high end option only slightly behind the 1080Ti. VRAM: 8 GB Memory bandwidth: 320 GBs/second Processing power: 2560 cores @ 1733 MHz NVIDIA GTX 1070 Ti: Priced at around $450 on Amazon. This is slightly less performant than the GTX 1080 but $100 cheaper. VRAM: 8 GB Memory bandwidth: 256 GBs/second Processing power: 2438 cores @ 1683 MHz NVIDIA GTX 1070: Priced at $380 on Amazon is currently the bestseller because of crypto miners. Somewhat slower than the 1080 GPUs but cheaper. VRAM: 8 GB Memory bandwidth: 256 GBs/second Processing power: 1920 cores @ 1683 MHz NVIDIA GTX 1060 6GB: Priced at around $290 on Amazon. Pretty cheap but the 6 GB VRAM limits you. Should be good for NLP but you’ll find the performance lacking in computer vision. VRAM: 6 GB Memory bandwidth: 216 GBs/second Processing power: 1280 cores @ 1708 MHz NVIDIA GTX 1050 Ti: Priced at around $200 on Amazon. This is the cheapest workable option. Good to get started with deep learning and explore if you’re new. VRAM: 4 GB Memory bandwidth: 112 GBs/second Processing power: 768 cores @ 1392 MHz NVIDIA Titan XP: The Titan XP is also an option but gives only a marginally better performance while being almost twice as expensive as the GTX 1080 Ti, it has 12 GB memory, 547.7 GB/s bandwidth and 3840 cores @ 1582 MHz. On a side note, NVIDIA Quadro GPUs are pretty expensive and don’t really help in deep learning they are more of use in CAD and working with heavy graphics production tasks. The graph below does a pretty good job of visualizing how all the GPUs above compare: Source: Slav Ivanov Blog, processing power is calculated as CUDA cores times the clock frequency Does the number of GPUs matter? Yes, it does. But how many do you really need? What’s going to suit the scale of your project without breaking your budget? 2 GPUs will always yield better results than just one - but it’s only really worth it if you need the extra power. There are two options you can take with multi-GPU deep learning. On the one hand, you can train several different models at once across your GPUs, or, alternatively distribute one single training model across multiple GPUs known as  “multi-GPU training”. The latter approach is compatible with TensorFlow, CNTK, and PyTorch. Both of these approaches have advantages. Ultimately, it depends on how many projects you’re working on and, again, what your needs are. Another important point to bear in mind is that if you’re using multiple GPUs, the processor and hard disk need to be fast enough to feed data continuously - otherwise the multi-GPU approach is pointless. Source: NVIDIA website It boils down to your needs and budget, GPUs aren’t exactly cheap.   Other heavy devices There are also other large machines apart from GPUs. These include the specialized supercomputer from NVIDIA, the DGX-2, and Tensor processing units (TPUs) from Google. The NVIDIA DGX-2 If you thought GPUs were expensive, let me introduce you to NVIDIA DGX-2, the successor to the NVIDIA DGX-1. It’s a highly specialized workstation; consider it a supercomputer that has been specially designed to tackle deep learning. The price of the DGX-2 is (*gasp*) $399,000. Wait, what? I could buy some new hot wheels for that, or Dual Intel Xeon Platinum 8168, 2.7 GHz, 24-cores, 16 NVIDIA GPUs, 1.5 terabytes of RAM, and nearly 32 terabytes of SSD storage! The performance here is 2 petaFLOPS. Let’s be real: many of us probably won’t be able to afford it. However, NVIDIA does have leasing options, should you choose to try it. Practically speaking, this kind of beast finds its use in research work. In fact, the first DGX-1 was gifted to OpenAI by NVIDIA to promote AI research. Visit the NVIDIA website for more on these monster machines. There are also personal solutions available like the NVIDIA DGX Workstation. TPUs Now that you’ve caught your breath after reading about AI dream machines, let’s look at TPUs. Unlike the DGX machines, TPUs run on the cloud. A TPU is what’s referred to as an application-specific integrated circuit (ASIC) that has been designed specifically for machine learning and deep learning by Google. Here’s the key stats: Cloud TPUs can provide up to 11.5 petaflops of performance in a single pod. If you want to learn more, go to Google’s website. When choosing GPUs you need to weigh up your options The GTX 1080 Ti is most commonly used by researchers and competitively for Kaggle, as it gives good value for money. Go for this if you are sure about what you want to do with deep learning. The GTX 1080 and GTX 1070 Ti are cheaper with less computing power, a more budget friendly option if you cannot afford the 1080 Ti. GTX 1070 saves you some more money but is slower. The GTX 1060 6GB and GTX 1050 Ti are good if you’re just starting off in the world of deep learning without burning a hole in your pockets. If you must have the absolute best GPU irrespective of the cost then the RTX 2080 Ti is your choice. It offers twice the performance for almost twice the cost of a 1080 Ti. Nvidia unveils a new Turing architecture: “The world’s first ray tracing GPU” Nvidia GPUs offer Kubernetes for accelerated deployments of Artificial Intelligence workloads Nvidia’s Volta Tensor Core GPU hits performance milestones. But is it the best?

Machine Learning introduces a huge potential to reduce costs and generate new revenue in an enterprise. Application of machine learning effectively helps in solving practical problems smartly within an organization. Machine learning automates tasks that would otherwise need to be performed by a live agent. It has made drastic improvements in the past few years, but many a time, a machine needs the assistance of a human to complete its task. This is why it is necessary for organizations to learn best practices in machine learning which you will learn in this article today. This article is an excerpt from a book written by Chiheb Chebbi titled Mastering Machine Learning for Penetration Testing Feature engineering in machine learning Feature engineering and feature selection are essential to every modern data science product, especially machine learning based projects. According to research, over 50% of the time spent building the model is occupied by cleaning, processing, and selecting the data required to train the model. It is your responsibility to design, represent, and select the features. Most machine learning algorithms cannot work on raw data. They are not smart enough to do so. Thus, feature engineering is needed, to transform data in its raw status into data that can be understood and consumed by algorithms. Professor Andrew Ng once said: "Coming up with features is difficult, time-consuming, requires expert knowledge. 'Applied machine learning' is basically feature engineering." Feature engineering is a process in the data preparation phase, according to the cross-industry standard process for data mining: The term Feature Engineering itself is not a formally defined term. It groups together all of the tasks for designing features to build intelligent systems. It plays an important role in the system. If you check data science competitions, I bet you have noticed that the competitors all use the same algorithms, but the winners perform the best feature engineering. If you want to enhance your data science and machine learning skills, I highly recommend that you visit and compete at www.kaggle.com: When searching for machine learning resources, you will face many different terminologies. To avoid any confusion, we need to distinguish between feature selection and feature engineering. Feature engineering transforms raw data into suitable features, while feature selection extracts necessary features from the engineered data. Featuring engineering is selecting the subset of all features, without including redundant or irrelevant features. Machine learning best practices Feature engineering enhances the performance of our machine learning system. We discuss some tips and best practices to build robust intelligent systems. Let's explore some of the best practices in the different aspects of machine learning projects. Information security datasets Data is a vital part of every machine learning model. To train models, we need to feed them datasets. While reading the earlier chapters, you will have noticed that to build an accurate and efficient machine learning model, you need a huge volume of data, even after cleaning data. Big companies with great amounts of available data use their internal datasets to build models, but small organizations, like startups, often struggle to acquire such a volume of data. International rules and regulations are making the mission harder because data privacy is an important aspect of information security. Every modern business must protect its users' data. To solve this problem, many institutions and organizations are delivering publicly available datasets, so that others can download them and build their models for educational or commercial use. Some information security datasets are as follows: The Controller Area Network (CAN) dataset for intrusion detection (OTIDS): http://ocslab.hksecurity.net/Dataset/CAN-intrusion-dataset The car-hacking dataset for intrusion detection: http://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset The web-hacking dataset for cyber criminal profiling: http://ocslab.hksecurity.net/Datasets/web-hacking-profiling The API-based malware detection system (APIMDS) dataset: http://ocslab.hksecurity.net/apimds-dataset The intrusion detection evaluation dataset (CICIDS2017): http://www.unb.ca/cic/datasets/ids-2017.html The Tor-nonTor dataset: http://www.unb.ca/cic/datasets/tor.html The Android adware and general malware dataset: http://www.unb.ca/cic/datasets/android-adware.html Use Project Jupyter The Jupyter Notebook is an open source web application used to create and share coding documents. I highly recommend it, especially for novice data scientists, for many reasons. It will give you the ability to code and visualize output directly. It is great for discovering and playing with data; exploring data is an important step to building machine learning models. Jupyter's official website is http://jupyter.org/: To install it using pip, simply type the following: python -m pip install --upgrade pip python -m pip install jupyter Speed up training with GPUs As you know, even with good feature engineering, training in machine learning is computationally expensive. The quickest way to train learning algorithms is to use graphics processing units (GPUs). Generally, though not in all cases, using GPUs is a wise decision for training models. In order to overcome CPU performance bottlenecks, the gather/scatter GPU architecture is best, performing parallel operations to speed up computing. TensorFlow supports the use of GPUs to train machine learning models. Hence, the devices are represented as strings; following is an example: "/device:GPU:0" : Your device GPU "/device:GPU:1" : 2nd GPU device on your Machine To use a GPU device in TensorFlow, you can add the following line: with tf.device('/device:GPU:0'): <What to Do Here> You can use a single GPU or multiple GPUs. Don't forget to install the CUDA toolkit, by using the following commands: Wget "http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.44-1_amd64.deb" sudo dpkg -i cuda-repo-ubuntu1604_8.0.44-1_amd64.deb sudo apt-get update sudo apt-get install cuda Install cuDNN as follows: sudo tar -xvf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" export CUDA_HOME=/usr/local/cuda Selecting models and learning curves To improve the performance of machine learning models, there are many hyper parameters to adjust. The more data that is used, the more errors that can happen. To work on these parameters, there is a method called GridSearchCV. It performs searches on predefined parameter values, through iterations. GridSearchCV uses the score() function, by default. To use it in scikit-learn, import it by using this line: from sklearn.grid_search import GridSearchCV Learning curves are used to understand the performance of a machine learning model. To use a learning curve in scikit-learn, import it to your Python project, as follows: from sklearn.learning_curve import learning_curve Machine learning architecture In the real world, data scientists do not find data to be as clean as the publicly available datasets. Real world data is stored by different means, and the data itself is shaped in different categories. Thus, machine learning practitioners need to build their own systems and pipelines to achieve their goals and train the models. A typical machine learning project respects the following architecture: Coding Good coding skills are very important to data science and machine learning. In addition to using effective linear algebra, statistics, and mathematics, data scientists should learn how to code properly. As a data scientist, you can choose from many programming languages, like Python, R, Java, and so on. Respecting coding's best practices is very helpful and highly recommended. Writing elegant, clean, and understandable code can be done through these tips: Comments are very important to understandable code. So, don't forget to comment your code, all of the time. Choose the right names for variables, functions, methods, packages, and modules. Use four spaces per indentation level. Structure your repository properly. Follow common style guidelines. If you use Python, you can follow this great aphorism, called the The Zen of Python, written by the legend, Tim Peters: "Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those!" Data handling Good data handling leads to successfully building machine learning projects. After loading a dataset, please make sure that all of the data has loaded properly, and that the reading process is performing correctly. After performing any operation on the dataset, check over the resulting dataset. Business contexts An intelligent system is highly connected to business aspects because, after all, you are using data science and machine learning to solve a business issue or to build a commercial product, or for getting useful insights from the data that is acquired, to make good decisions. Identifying the right problems and asking the right questions are important when building your machine learning model, in order to solve business issues. In this tutorial, we had a look at somes tips and best practices to build intelligent systems using Machine Learning. To become a master at penetration testing using machine learning with Python,  check out this book  Mastering Machine Learning for Penetration Testing Why TensorFlow always tops machine learning and artificial intelligence tool surveys Intelligent Edge Analytics: 7 ways machine learning is driving edge computing adoption in 2018 Tackle trolls with Machine Learning bots: Filtering out inappropriate content just got easy

Machine learning as a service (MLaaS) is an innovation that is growing out of 2 of the most important tech trends - cloud and machine learning. It's significant because it enhances both. It makes cloud an even more compelling proposition for businesses. That's because cloud typically has three major operations: computing, networking and storage. When you bring machine learning into the picture, the data that cloud stores and processes can be used in radically different ways, solving a range of business problems. What is machine learning as a service? Cloud platforms have always competed to be the first or the best to provide new services. This includes platform as a service (PaaS) solutions, infrastructure as a service (IaaS) solutions and software as a service (SaaS) solutions. In essense, cloud providers like AWS and Azure provide sets of software to different things so their customers don't have to. Machine learning as a service is simply another instance of the services offered by cloud providers. It could include a wide range of features, from data visualization to predictive analytics and natural language processing. It makes running machine learning models easy, effectively automating some of the work that might have typically done manually by a data engineering team. Here are the biggest cloud providers who offer machine learning as a service: Google Cloud Platform Amazon Web Services Microsoft Azure IBM Cloud Every platform provides a different suite of services and features. It will ultimately depend on what's most important to you which one you choose. Let's take a look now at the key differences between these cloud providers' machine learning as a service offerings. Comparing the leading MLaaS products Google Cloud AI Google Cloud Platform has always provided their own services to help businesses grow. They provide modern machine learning services with pre-trained models and a service to generate your own tailored models. Majority of Google applications like Photos (image search), the Google app (voice search), and Inbox (Smart Reply) have been built using the same services that they provide to their users. Pros: Cheaper in comparison to other Cloud providers Provides IaaS and PaaS Solutions Cons: Google Prediction API is going to be discontinued (May 1st, 2018) Lacks a visual interface You'll need to know TensorFlow Amazon Machine Learning Amazon Machine Learning provides services for building ML models and generating predictions which help users develop robust, scalable, and cost-effective smart applications. With the help of Amazon Machine Learning you are able to use powerful machine learning technology without having any prior experience in machine learning algorithms and techniques. Pros: Provides versatile automated solutions It's accessible - users don't need to be machine learning experts Cons: The more you use, the more expensive it is Azure Machine Learning Studio Microsoft Azure provides you with Machine Learning Studio - a simple browser-based, drag-and-drop environment which functions without any kind of coding. You are provided with fully-managed cloud services that enable you to easily build, deploy and share predictive analytics solutions. Here you are also provided with a platform (Gallery) to share and contribute to the community. Pros: Consists of most versatile toolset for MLaaS You can contribute to and reuse machine learning solutions from the community Cons: Comparatively expensive A lot of manual work is required Watson Machine Learning Similar to the above platforms, IBM Watson Machine Learning is a service that helps  users to create, train, and deploy self-learning models to integrate predictive capabilities within their applications. This platform provides automated and collaborative workflows to grow intelligent business applications. Pros: Automated workflows Data science skills is not necessary Cons: Comparatively limited APIs and services Lacks streaming analytics Selecting the machine learning as a service solution that's right for you There are so many machine learning as a service solutions out there that it's easy to get confused. The crucial step to take before you make a decision to purchase anything is to plan your business requirements. Think carefully not only about what you want to achieve, but what you already do too. You want your MLaaS solution to easily integrate into the way you currently work. You also don't want it to replicate any work you're currently doing that you're pretty happy with. It gets repeated so much but it remains as true as it has ever been - make sure your software decisions are fully aligned with your business needs. It's easy to get seduced by the promise of innovative new tools, but without the right alignment they're not going to help you at all.

The use of AI (Artificial Intelligence) technology in commercial aviation has brought some significant changes in the ways flights are being operated today. World’s leading airliner service providers are now using AI tools and technologies to deliver a more personalized traveling experience to their customers. From building AI-powered airport kiosks to using it for automating airline operations and security checking, AI will play even more critical roles in the aviation industry. Engineers have found AI can help the aviation industry with machine vision, machine learning, robotics, and natural language processing. Artificial intelligence has been found to be highly potent and various researches have shown how the use of artificial intelligence can bring significant changes in aviation. Few airlines now use artificial intelligence for predictive analytics, pattern recognition, auto scheduling, targeted advertising, and customer feedback analysis showing promising results for better flight experience. A recent report shows that aviation professionals are thinking to use artificial intelligence to monitor pilot voices for a hassle-free flying experience of the passengers. This technology is to bring huge changes in the world of aviation. Identification of the Passengers There’s no need to explain how modern inventions are contributing towards the betterment of mankind and AI can help in air transportation in numerous ways. Check-in before boarding is a vital task for an airline and they can simply take the help of artificial intelligence to do it easily, the same technology can be also used for identifying the passengers as well. American airline company Delta Airlines took the initiative in 2017. Their online check-in via Delta mobile app and ticketing kiosks have shown promising results and nowadays you can see many airlines taking similar features to the whole new level. The Transportation Security Administration of the United States has introduced new AI technology to identify potential threats at the John F. Kennedy, Los Angeles International Airport and Phoenix airports. Likewise, Hartsfield-Jackson Airport is planning to launch America’s first biometric terminal. Once installed, “the AI technology will make the process of passenger identification fast and easy for officials. Security scanners, biometric identification”, and machine learning are some of the AI technologies that will make a number of jobs easy for us. In this way, AI helps us predict disruption in airline services. Baggage Screening Baggage screening is another tedious but important task that needs to be done at the airport. However, AI has simplified the process of baggage screening. The American airlines once conducted a competition on app development on artificial intelligence and Team Avatar became the winner of the competition for making an app that would allow the users to determine the size of their baggage at the airport. Osaka Airport in Japan is planning to install the Syntech ONE 200, which is an AI technology developed to screen baggage for multiple passenger lanes. Such tools will not only automate the process of baggage screening but also help authorities detect illegal items effectively. Syntech ONE 200is compatible with the X-ray security system and it increases the probability of identification of potential threats. Assisting Customers AI can be used to assist customers in the airport and it can help a company reduce its operational costs and labor costs at the same time. Airlines companies are now using AI technologies to help their customers to resolve issues quickly by getting accurate information on future flights trips on their internet-enabled devices. More than 52% of airlines companies across the world have planned to install AI-based tools to improve their customer service functions in the next five years. Artificial Intelligence can answer various common questions of the customers, assisting them for check-in requests, the status of the flight and more. Nowadays artificial intelligence is also used in air cargo for different purposes such as revenue management, safety, and maintenance and it has shown impressive results till date. Maintenance Prediction Airlines companies are planning to implement AI technology to predict potential failures of maintenance on aircraft. Leading aircraft manufacturer Airbus is taking measures to improve the reliability of aircraft maintenance. They are using Skywise, a cloud-based data storing system. It helps the fleet to collect and record a huge amount of real-time data. The use of AI in the predictive maintenance analytics will pave the way for a systematic approach on how and when the aircraft maintenance should be done.  Nowadays you can see how top-rated airlines use artificial intelligence to make the process of maintenance easy and improve the user experience at the same time. Pitfalls of using AI in Aviation Despite being considered as a future of the aviation industry,  AI has some pitfalls. For instance, it takes time for implementation and it cannot be used as an ideal tool for customer service. The recent incident of Ethiopian Airlines Boeing 737 was an eye-opener for us and it clearly represents the drawback of AI technology in the aviation sector. The Boeing 737 crashed a few minutes after it took off from the capital of Ethiopia. The failure of the MCAC system was the key reasons behind the fatal accident. Also, AI is quite expensive; for example, if an airline company is planning to deploy a chatbot, it will have to invest more than $15,000. Thus, it would be a hard thing for small companies to invest for the same and this could create a barrier between small and big airlines in the future. As the market is becoming highly competitive, big airlines will conquer the market and the small airlines might face an existential threat due to this reason.   Conclusion The use of artificial intelligence in aviation has made many tasks easy for airlines and airport authorities across the world. From identifying passengers to screening the bags and providing fast and efficient customer care solutions. Unlike the software industry, the risks of real life harms are exponentially higher in the aviation industry. While other industries have started using this technology long back, the adoption of AI in aviation has been one of caution, and rightly so. As the aviation industry embraces the benefits of artificial intelligence and machine learning, it must also invest in putting in place checks and balances to identify, reduce and eliminate harmful consequences of AI, whether intended or otherwise.  As Silicon Valley reels in ethical dilemmas, the aviation industry will do well to learn from Silicon Valley while making a transition to a smart future. The aviation industry known for its rigorous safety measures and processes may, in fact, have a thing or two to teach Silicon Valley when it comes to designing, adopting and deploying AI systems into live systems that have high-risk profiles. Author Bio Maria Brown is Content Writer, Blogger and maintaining Social Media Optimization for 21Twelve Interactive. She believes in sharing her solid knowledge base with a focus on entrepreneurship and business. You can find her on Twitter.

The most common programming languages currently used for AI and machine learning development are Python, R, Scala, Go, among others with the latest addition being Julia. Functional languages as old as Lisp and Haskell were used to implement machine learning algorithms decades ago when AI was an obscure research area of interest. There wasn’t enough hardware and software advancements back them for implementations. Some commonalities in all of the above language options are that they are simple to understand and promote clarity. They use fewer lines of code and lend themselves well to the functional programming paradigm. What is Functional programming? Functional programming is a programming approach that uses logical functions or procedures within its programming structure. It means that the programming is done with expressions instead of statements. In a functional programming (FP) approach, computations are treated as evaluations of mathematical functions and it mostly deals with immutable data. In other words, the state of the program does not change, the functions or procedures are fixed and the output value of a function depends solely on the arguments passed to it. Let’s look at the characteristics of a functional programming approach before we see why they are well suited for developing artificial intelligence programs. Functional programming features Before we see functional programming in a machine learning context, let’s look at some of its characteristics. Immutable: If a variable x is declared and used in the program, the value of the variable is never changed later anywhere in the program. Each time the variable x is called, it will return the same value assigned originally. This makes it pretty straightforward, eliminating the need to think of state change throughout the program. Referential transparency: This means that an expression or computation always results in the same value in any part/context of the program. A referentially transparent programming language’s programs can be manipulated as algebraic equations. Lazy evaluation: Being referentially transparent, the computations yield the same result irrespective of when they are performed. This enables to postpone the computation of values until they are required/called. This means one could evaluate them lazily. Lazy evaluation helps avoids unnecessary computations and saves memory. Parallel programming: Since there is no state change due to immutable variables, the functions in a functional program can work in parallel as instructions. Parallel loops can be easily expressed with good reusability. Higher-order functions: A higher order function can take one or more functions as arguments. They may also be able to return a function as their result. Higher-order functions are useful for refactoring code and to reduce repetition. The map function found in many programming languages is an example of a higher-order function. What kind of programming is good for AI development? Machine learning is a sub-domain of artificial intelligence which deals with concepts of making predictions from data, take actions without being explicitly programmed, recommendation systems and so on. Any programming approach that focuses on logic and mathematical functions is good for artificial intelligence (AI). Once the data is collected and prepared it is time to build your machine learning model.. This typically entails choosing a model, then training and testing the model with the data. Once the desired accuracy/results are achieved, then the model is deployed. Training on the data requires data to be consistent and the code to be able to communicate directly with the data without much abstraction for least unexpected errors. For AI programs to work well, the language needs to have a low level implementation for faster communication with the processor. This is why many machine learning libraries are created in C++ to achieve fast performance. OOP with its mutable objects and object creation is better suited for high-level production software development, not very useful in AI programs which works with algorithms and data. As AI is heavily based on math, languages like Python and R are widely used languages in AI currently. R lies more towards statistical data analysis but does support machine learning and neural network packages. Python being faster for mathematical computations and with support for numerical packages is used more commonly in machine learning and artificial intelligence. Why is functional programming good for artificial intelligence? There are some benefits of functional programming that make it suitable for AI. It is closely aligned to mathematical thinking, and the expressions are in a format close to mathematical definitions. There are few or no side-effects of using a functional approach to coding, one function does not influence the other unless explicitly passed. This proves to be great for concurrency, parallelization and even debugging. Less code and more consistency The functional approach uses fewer lines of code, without sacrificing clarity. More time is spent in thinking out the functions than actually writing the lines of code. But the end result is more productivity with the created functions and easier maintenance since there are fewer lines of code. AI programs consist of lots of matrix multiplications. Functional programming is good at this just like GPUs. You work with datasets in AI with some algorithms to make changes in the data to get modified data. A function on a value to get a new value is similar to what functional programming does. It is important for the variables/data to remain the same when working through a program. Different algorithms may need to be run on the same data and the values need to be the same. Immutability is well-suited for that kind of job. Simple approach, fast computations The characteristics/features of functional programming make it a good approach to be used in artificial intelligence applications. AI can do without objects and classes of an object oriented programming (OOP) approach, it needs fast computations and expects the variables to be the same after computations so that the operations made on the data set are consistent. Some of the popular functional programming languages are R, Lisp, and Haskell. The latter two are pretty old languages and are not used very commonly. Python can be used as both, functional and object oriented. Currently, Python is the language most commonly used for AI and machine learning because of its simplicity and available libraries. Especially the scikit-learn library provides support for a lot of AI-related projects. FP is fault tolerant and important for AI Functional programming features make programs fault tolerant and fast for critical computations and rapid decision making. As of now, there may not be many such applications but think of the future, systems for self-driving cars, security, and defense systems. Any fault in such systems would have serious effects. Immutability makes the system more reliable, lazy evaluation helps conserve memory, parallel programming makes the system faster. The ability to pass a function as an argument saves a lot of time and enables more functionality. These features of functional programming make it a fitting choice for artificial intelligence. To further understand why use functional programming for machine learning, read the case made for using the functional programming language Haskell for AI in the Haskell Blog. Why functional programming in Python matters: Interview with best selling author, Steven Lott Grain: A new functional programming language that compiles to Webassembly 8 ways Artificial Intelligence can improve DevOps

Cybersecurity analysts have warned that spoofing using artificial intelligence is within the realm of possibility and that people should be aware of the possibility of getting fooled with such voice or picture-based deepfakes. What is Deepfake? Deepfakes rely on a branch of AI called Generative Adversarial Networks (GANs). It requires two machine learning networks that teach each other with an ongoing feedback loop. The first one takes real content and alters it. Then, the second machine learning network, known as the discriminator, tests the authenticity of the changes. As the machine learning networks keep passing the material back and forth and receiving feedback about it, they get smarter. GANs are still in the early stages, but people expect numerous potential commercial applications. For example, some can convert a single image into different poses. Others can suggest outfits similar to what a celebrity wears in a photo or turn a low-quality picture into a high-resolution snapshot. But, outside of those helpful uses, deepfakes could have sinister purposes. Consider the blowback if a criminal creates a deepfake video of something that would hurt someone's reputation — for instance, a deepfake video of a politician "admitting" to illegal activities, like accepting a bribe. Other instances of this kind of AI that are already possible include cases of misleading spoken dialogue. Then, the lips of someone saying something offensive get placed onto someone else. In one of the best-known examples of Deepfake manipulation, BuzzFeed published a clip now widely known as "ObamaPeele." It combined a video of President Obama with film director Jordan Peele's lips. The result made it seem as if Obama cursed and said things he never would in public. Deepfakes are real enough to cause action The advanced deepfake efforts that cybersecurity analysts warn about rely on AI to create something so real that it causes people to act. For example, in March of 2019, the CEO of a British energy firm received a call from what sounded like his boss. The message was urgent — the executive needed to transfer a large amount of funds to a Hungarian supplier within the hour. Only after the money was sent did it become clear the executive’s boss was never on the line. Instead, cybercriminals had used AI to generate an audio clip that mimicked his boss’s voice. The criminals called the British man and played the clip, convincing him to transfer the funds. The unnamed victim was scammed out of €220,000 — an amount equal to $243,000. Reports indicate it's the first successful hack of its kind, although it's an unusual way for hackers to go about fooling victims. Some analysts point out other hacks like this may have happened but have gone unreported, or perhaps the people involved did not know hackers used this technology. According to Rüdiger Kirsch, a fraud expert at the insurance company that covered the full amount of the claim, this is the first time the insurer dealt with such an instance. The AI technology apparently used to mimic the voice was so authentic that it captured the parent company leader's German accent and the melody of his voice. Deepfakes capitalize on urgency One of the telltale signs of deepfakes and other kinds of spoofing — most of which currently happen online — is a false sense of urgency. For example, lottery scammers emphasize that their victims must send personal details immediately to avoid missing out on their prizes. The deepfake hackers used time constraints to fool this CEO, as well. The AI technology on the other end of the phone told the CEO that he needed to send the money to a Hungarian supplier within the hour, and he complied. Even more frighteningly, the deceiving tech was so advanced that hackers used it for several phone calls to the victim. One of the best ways to avoid scams is to get further verification from outside sources, rather than immediately responding to the person engaging with you. For example, if you're at work and get a call or email from someone in accounting who asks for your Social Security number or bank account details to update their records, the safest thing to do is to contact the accounting department yourself and verify the legitimacy. Many online spoofing attempts have spelling or grammatical errors, too. The challenging thing about voice trickery, though, is that those characteristics don't apply. You can only go by what your ears tell you. Since these kinds of attacks are not yet widespread, the safest thing to do for avoiding disastrous consequences is to ignore the urgency and take the time you need to verify the requests through other sources. Hackers can target deepfake victims indefinitely One of the most impressive things about this AI deepfake case is that it involved more than one phone conversation. The criminals called again after receiving the funds to say that the parent company had sent reimbursement funds to the United Kingdom firm. But, they didn't stop there. The CEO received a third call that impersonated the parent company representative again and requested another payment. That time, though, the CEO became suspicious and didn't agree. As, the promised reimbursed funds had not yet come through. Moreover, the latest call requesting funds originated from an Austrian phone number. Eventually, the CEO called his boss and discovered the fakery by handling calls from both the real person and the imposter simultaneously. Evidence suggests the hackers used commercially available voice generation software to pull off their attack. However, it is not clear if the hackers used bots to respond when the victim asked questions of the caller posing as the parent company representative. Why do deepfakes work so well? This deepfake is undoubtedly more involved than the emails hackers send out in bulk, hoping to fool some unsuspecting victims. Even those that use company logos, fonts and familiar phrases are arguably not as realistic as something that mimics a person's voice so well that the victim can't distinguish the fake from the real thing. The novelty of these incidents also makes individuals less aware that they could happen. Although many people receive training that helps them spot some online scams, the curriculum does not yet extend to these advanced deepfake cases. Making the caller someone in a position of power increases the likelihood of compliance, too. Generally, if a person hears a voice on the other end of the phone that they recognize as their superior, they won't question it. Plus, they might worry that any delays in fulfilling the caller's request might get perceived as them showing a lack of trust in their boss or an unwillingness to follow orders. You've probably heard people say, "I'll believe it when I see it." But, thanks to this emerging deepfake technology, you can't necessarily confirm the authenticity of something by hearing or seeing it. That's an unfortunate development, plus something that highlights how important it is to investigate further before acting. That may mean checking facts or sources or getting in touch with superiors directly to verify what they want you to do. Indeed, those extra steps take more time. But, they could save you from getting fooled. Author Bio Kayla Matthews writes about big data, cybersecurity, and technology. You can find her work on The Week, Information Age, KDnuggets and CloudTweaks, or over at ProductivityBytes.com. Media manipulation by Deepfakes and cheap fakes require both AI and social fixes, finds a Data & Society report Terrifyingly realistic Deepfake video of Bill Hader transforming into Tom Cruise is going viral on YouTube Now there is a Deepfake that can animate your face with just your voice and a picture using Temporal GANs

The rise of the bots is nigh! If you can imagine a situation involving a dialog, there is probably a chatbot for that. Just look at the chatbot market - text-based email/SMS bots, voice-based bots, bots for customer support, transaction-based bots, entertainment bots and many others. A large number of enterprises, from startups to established organizations, are seeking to invest in this sector. This has also led to an increase in the number of platforms used for chatbot building. These frameworks incorporate AI techniques along with natural language processing capabilities to assist developers in building and deploying chatbots. Let’s start with how a chatbot typically works before diving into some of the frameworks. Understand: The first step for any chatbot is to understand the user input. This is made possible using pattern matching and intent classification techniques. ‘Intents’ are the tasks that users might want to perform with a chatbot. Machine learning, NLP and speech recognition techniques are typically used to identify the intent of the message and extract named entities. Entities are the specific pieces of information extracted from the user’s response i.e. the content associated with an intent. Respond: After understanding, the next goal is to generate a response. This is based on the current input message and the context of the conversation. After specifying the intents and entities, a dialog flow is constructed. This is basically the replies/feedback expected from a chatbot. Learn: Chatbots use AI techniques such as natural language understanding and pattern recognition to store and distinguish between the context of the information provided, and elicit a suitable response for future replies. This is important because different requests might have different meanings depending on previous requests. Top chatbot development frameworks A bot development framework is a set of predefined classes, functions, and utilities that a developer can use to build chatbots easier and faster. They vary in the level of complexity, integration capabilities, and functionalities. Let us look at some of the development platforms utilized for chatbot building. API.AI API.AI, a code based framework with a simple web-based interface, allows users to build engaging voice and text-based conversational apps using a large number of libraries and SDKs including Android, iOS, Webkit HTML5, Node.js, and Python API. It also supports nearly 32 one-click platform integrations such as Google, Facebook Messenger, Twitter and Skype to name a few. API.AI makes use of an agent - a container that transforms natural language based user requests into actionable data. The software tries to find the intent behind a user’s reply and matches it to the default or the closest match. After intent matching, it executes the actions and responses the developer has defined for that intent. API.AI also makes use of entities. Once the intents and entities are specified, the bot is trained. API.AI’s training module efficiently tracks each user’s request and lets developers see how they are parsed and matched to an intent. It also allows for correction of any errors and change requests thus retraining the bot. API.AI streamlines the entire bot-creating process by helping developers provide domain-specific knowledge that is unique to a bot’s needs while working on speech recognition, intent and context management in the backend. Google has recently partnered with API.AI to help them build conversational tools like Apple’s Siri. Microsoft Bot Framework Microsoft Bot Framework allows building and deployment of chatbots across multiple platforms and services such as web, SMS, non-Microsoft platforms, Office 365, Skype etc. The Bot Framework includes two components - The Bot Builder and the Microsoft Cognitive Services. The Bot Builder comprises of two full-featured SDKs - for the.NET and the Node.js platforms along with an emulator for testing and debugging. There’s also a set of RESTful APIs for building code in other languages. The SDKs support features for simple and easy interactions between bots. They also have a large collection of prebuilt sample bots for the developer to choose from. The Microsoft Cognitive Services is a collection of intelligent APIs that simplify a variety of AI tasks such as allowing the system to understand and interpret the user's needs using natural language in just a few lines of code. These APIs allow integration to most modern languages and platforms and constantly improve, learn, and get smarter. Microsoft created the AI Inner Circle Partner Program to work hand in hand with industry to create AI solutions. Their only partner in the UK is ICS.AI who build conversational AI solutions for the UK's public sector. ICS are the first choice for many organisations due to their smart solutions that scale and serve to improve services for the general public. Developers can build bots in the Bot Builder SDK using C# or Node.js. They can then add AI capabilities with Cognitive Services. Finally, they can register the bots on the developer portal, connecting it to users across platforms such as Facebook and Microsoft Teams and also deploy it on the cloud like Microsoft Azure. For a step-by-step guide for chatbot building using Microsoft Bot Framework, you can refer to one of our books on the topic. Sabre Corporation, a customer service provider for travel agencies, have recently announced the development of an AI-powered chatbot that leverages Microsoft Bot Framework and Microsoft Cognitive Services. Watson Conversation IBM’s Watson Conversation helps build chatbot solutions that understand natural-language input and use machine learning to respond to customers in a way that simulates conversations between humans. It is built on a neural network of one million Wikipedia words. It offers deployment across a variety of platforms including mobile devices, messaging platforms, and robots. The platform is robust and secure as IBM allows users to opt out of data sharing. The IBM Watson Tone Analyzer service can help bots understand the tone of the user’s input for better management of the experience. The basic steps to create a chatbot using Watson Conversation are as follows. We first create a workspace - a place for configuring information to maintain separate intents, user examples, entities, and dialogues for each application. One workspace corresponds to one bot. Next, we create Intents. Watson Conversation makes use of multiple conditioned responses to distinguish between similar intents. For example, instead of building specific intents for locations of different places, it creates a general intent “location” and adds an entity to capture the response, like the “location- bedroom” - to the right, near the stairs, “location-kitchen”- to the left. The third step is entity establishment. This involves grouping entities that might trigger a similar response in the dialog. The dialog flow, thus generated after specifying the intents and entities, goes through testing followed by embedding this into an application. It is then connected with other services by using the conversation API. Staples, an office supply retailing firm, uses Watson Conversation in their “Easy Systems” to simplify the customer’s shopping experience. CXP Designer and Aspect NLU Aspect Customer Experience Platform is an application lifecycle management tool to build text and voice-based applications such as chatbots. It provides deployment options across multiple communication channels like text, voice, mobile web and social media networks. The Aspect CXP typically includes a CXP designer to build chatbots and the inbuilt Aspect NLU to provide advanced natural language capabilities. CXP designer works by creating dialog objects to provide a menu of options for frontend as well as backend. Menu items for the frontend are used to create intents and modules within those intents. The developer can then modify labels (of those intents and modules) manually or use the Aspect NLU to disambiguate similar questions for successful extraction of meaning and intent. The Aspect NLU includes tools for spelling correction, linguistic lexicons such as nouns, verbs etc. and options for detecting and extracting common data types such as date, time, numbers, etc. It also allows a developer to modify the meaning extraction based on how they want it if they want it! CXP designer also allows skipping of certain steps in chatbots. For instance, if the user has already provided their tracking id for a particular package, the chatbot will skip the prompt of asking them the tracking id again. With Aspect CXP, developers can create and deploy complex chatbots. Radisson Blu Edwardian, a hotel in London, has collaborated with Aspect software to build an SMS based, AI virtual host. Conclusion Another popular chatbot development platform worth mentioning is the Facebook messenger with over 100,000 monthly active bots, but without cross-platform deployment features. The above bot frameworks are typically used by developers to build chatbots from scratch and require some programming skills. However, there has been a rise in automated bot development tools of late. Some of these include Chatfuel and Motion AI and typically involve drag and drop functionalities. With such tools, beginners and non-programmers can create and deploy chatbots within few minutes. But, they lack the extended functionalities supported by typical code based frameworks such as the flexibility to store data, produce analytics or incorporate customized AI tasks. Every chatbot development system, whether framework or tool, serves a different purpose. Choosing the right one depends on the type of application to build, organizational needs, and the developer’s expertise.

Internet job portal ‘Indeed.com’ links potential employers with people who are looking to take the next step in their careers. The proportion of job posts on their site, relating to ‘Data Science’, a specific job in the AI category, is growing fast (see chart below). More broadly, Artificial Intelligence & machine learning skills, of which ‘Data Scientist’ is just one example, are in demand. No wonder that it has been termed as the sexiest job role of the 21st century. Interest comes from an explosion of jobs in the field from big companies and Start-Ups, all of which are competing to come up with the best AI business and to earn the money that comes with software that automates tasks. The skills shortage associated with Artificial Intelligence represents an opportunity for any developer. There has never been a better time to consider whether reskilling or upskilling in AI could be a lucrative path for you. Below : Indeed.com. Proportion of job postings containing Data Scientist or Data Science. [caption id="attachment_21240" align="aligncenter" width="1525"] Artificial Intelligence skills are increasingly in demand and create a real opportunity for those prepared to reskill or upskill.[/caption] Source: Indeed  The AI skills gap the market is experiencing comes from the difficulty associated with finding an individual demonstrating a competent mixture of the very disparate faculties that AI roles require. Artificial Intelligence and it’s near equivalents such as Machine Learning and Neural Networks operate at the intersection of what have mostly been two very different disciplines – statistics and software development. In simple terms, they are half coding, half maths. Hamish Ogilvy, CEO of AI based Internal Search company Sajari is all too familiar with the problem. He’s on the front line, hiring AI developers. “The hardest part”, says Ogilvy, “is that AI is pretty complex and the average developer/engineer does not have the background in maths/stats/science to actually understand what is happening. On the flip side the trouble with the stats/maths/science people is that they typically can't code, so finding people in that sweet spot that have both is pretty tough.” He’s right. The New York Times suggests that the pool of qualified talent is only 10,000 people, worldwide. Those who do have jobs are typically happily ensconced, paid well, treated appropriately and given no reason whatsoever to want to leave. [caption id="attachment_21244" align="aligncenter" width="1920"] Judged by $ investments in the area alone, AI skills are worth developing for those wishing to stay current on technology skills.[/caption] In fact, an instinct to develop AI skills will serve any technology employee well. No One can have escaped the many estimates, from reputable consultancies, suggesting that Automation will replace up to 30% of jobs in the next 10 years. No job is safe. Every industry is touched by AI in some form. Any responsible individual with a view to the management of their own skills could learn ML and AI skills to stay relevant in current times. Even if you don't want to move out of your current job, learning ML will probably help you adapt better in your industry. What is a typical AI job and what will it pay? OpenAI, a world class Artificial Intelligence research laboratory, revealed the salaries of some of its key Data Science employees recently. Those working in the AI field with a specialization can earn $300 to $500k in their first year out of university. Experts in Artificial Intelligence now command salaries of up to $1m. [caption id="attachment_21242" align="aligncenter" width="432"] The New York Times observes AI salaries[/caption] [caption id="attachment_21241" align="aligncenter" width="1121"] The New York Times observes AI salaries[/caption] Source: The New York times Indraneil Roy, an Expert in AI and Talent Acquisition who works for Edge Networks puts it this way when outlining the difficulties of hiring the right skills and to explain why wages in the field are so high. “The challenge is the quality of resources. As demand is high for this skill, we are already seeing candidates with fake experience and work pedigree not up to standards.” The phenomenon is also causing a ‘brain drain’ in Universities. About a third of jobs in the AI field will go to someone with a Ph.D., and all of those are drawn from universities working on the discipline, often lured by the significant pay packages which are available. So, with huge demand and the universities drained, where will future AI employees come from? 3 ways to skill up to become an AI expert (And earn all that money?) There is still not a lot of agreed terminology or even job roles and responsibility in the sector. However, some things are clear. Those wishing to evolve in to the field of AI must understand the conceptual thinking involved, as a starting point, whether that view is found on the job or as part of an informal / formal educational course. Specifically, most jobs in the specialty require a working knowledge of neural networks, data / analytics, predictive analytics, with some basic programming and database skills. There are some great resources available online to train you up. Most, as you’d expect, are available on your smartphone so there really is no excuse for not having a look. 1. Free online course: Machine Learning & Statistics and probability Hamish Ogilvy summed the online education which is available in the area well. There are “so many free courses now on AI from Stanford,” he said, “that people are able to educate themselves and make up for the failings of antiquated university courses. AI is just maths really,” he says “complex models and stats. So that's what people need grounding in to be successful.” Microsoft offer free AI courses for technical professionals: Microsoft’s training materials are second to none. They’re also provided free and provide a shortcut to a credible understanding in an area simply because it comes from a technical behemoth. Importantly, they also have a list of AI services which you can play with, again for free. For example, a Natural Language engine offers a facility for you to submit text from Instant Messaging conversations and establish the sentiment being felt by the writer. Practical experience of the tools, processes and concepts involved will set you apart. See below. [caption id="attachment_21245" align="aligncenter" width="1999"] Check out Microsoft’s free AI training program for developers.[/caption] Google are taking a proactive stance on Machine Learning. They see it’s potential to improve efficiency in every industry and also offer free ML training courses on their site. 2. Take courses on AI/ML Packt’s machine learning courses, books and videos: Packt is working towards a mission to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. It has published over 6,000 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools. You can choose from a variety of Packt’s books, videos and courses for AI/ML. Here’s a list of top ones: Artificial Intelligence by Example [Book] Artificial Intelligence for Big data [Book] Learn Artificial Intelligence with TensorFlow [Video] Introduction to Artificial Intelligence with Java [Video] Advanced Artificial Intelligence Projects with Python [Video] Python Machine learning - Second Edition [Book] Machine Learning with R - Second Edition [Book] Coursera’s machine learning courses Coursera is a company which make training courses, for a variety of subjects, available online. Taken from actual University course content and delivered with tests, videos and training notes, all accessed online, each course is roughly a University Module. Students pick up an ‘up to under-graduate’ level of understanding of the content involved. Coursera’s courses are often cited as merit worthy and are recognizable in the industry. Costs vary but are typically between $2k and $5k per course. 3. Learn by doing Familiarize yourself with relevant frameworks and tools including Tensor Flow, Python and Keras. TensorFlow from Google is the most used open source AI software library. You can use existing code in your experiments and experiment with neural networks in much the same way as you can in Microsoft’s. Python is a programming language written for a big data world. Its proponents will tell you that Python saves developers hundreds of lines of code, allowing you to tie together information and systems faster than ever before. Python is used extensively in ML and AI applications and should be at the top of your study list. Keras, a deep learning library is similarly ubiquitous. It’s a high level Neural Network API designed to allow prototyping of your software as fast as possible. Finally, a lesser known but still valuable resources is the Accord.net. It is one final example of the many  software elements with which you can engage with to train yourself up. Accord Framework.net will expose you to image libraries, natural learning and real time facial recognition. Earn extra points with employers AI has several lighthouse tasks which are proving the potential of the technology in these still early stages. We’ve included a couple of examples, Natural Language processing and image recognition, above. Practical expertise in these areas specifically, image or voice recognition or pattern matching are valued highly by employers. Alternatively, have you patented something? A registered patent in your name is highly prized. Especially something to do with Machine Learning. Both will help you showcase Extra skills / achievements that will help your application.’ The specifics of how to apply for patents differ by country but you can find out more about the overall principles of how to submit an idea here. Passion and engagement in the subject are also, clearly appealing characteristics for potential employers to see in applicants. Participating in competitions like Kaggle, and having a portfolio of projects you can showcase on facilities like GitHub are also well prized. Of all of these suggestions, for those employed, any on the job experience you can get will stand you in the best stead. Indraneil says "Individual candidates need to spend more time doing relevant projects while in employment. Start ups involved in building products and platforms on AI seem to have better talent." The fact that there are not many AI specialists around is a bad sign There is a demand for employees with AI skills and an investment in relevant training may pay you well. Unfortunately, the underlying problem this situation reveals could be far worse than the problems experienced so far. Together, once found, all these AI scientists are going to automate millions of jobs, in every industry, in every country around the world. If Industry, Governments and Universities cannot train enough people to fill the roles being created by an evolving skills market, we may rightly be concerned to worry about how they will deal with retraining all those displaced by AI, for whom there may be no obvious replacement role available. 18 striking AI Trends to watch in 2018 – Part 1 DeepMind, Elon Musk, and others pledge not to build lethal AI Attention designers, Artificial Intelligence can now create realistic virtual textures

The ethics of artificial intelligence seems to have found its way into just about every corner of public life. From law enforcement to justice, through to recruitment, artificial intelligence is both impacting both the work we do and the way we think. But if you really want to get into the ethics of artificial intelligence you need to go further than the public realm and move into the bedroom. Sex robots have quietly been a topic of conversation for a number of years, but with the rise of artificial intelligence they appear to have found their way into the mainstream - or at least the edges of the mainstream. There’s potentially some squeamishness when thinking about sex robots, but, in fact, if we want to think seriously about the consequences of artificial intelligence - from how it is built to how it impacts the way we interact with each other and other things - sex robots are a great place to begin. Read next: Introducing Deon, a tool for data scientists to add an ethics checklist Sexualizing artificial intelligence It’s easy to get caught up in the image of a sex doll, plastic skinned, impossible breasts and empty eyes, sad and uncanny, but sexualized artificial intelligence can come in many other forms too. Let’s start with sex chatbots. These are, fundamentally, a robotic intelligence that is able to respond to and stimulate a human’s desires. But what’s significant is that they treat the data of sex and sexuality as primarily linguistic - the language people use to describe themselves, their wants, their needs their feelings. The movie Her is a great example of a sexualised chatbot. Of course, the digital assistant doesn’t begin sexualised, but Joaquin Phoenix ends up falling in love with his female-voiced digital assistant through conversation and intimate interaction. The physical aspect of sex is something that only comes later. https://youtu.be/3n5muEWaE_Q Ai Furuse - the Japanese sex chatbot But they exist in real life too. The best example out these is Ai Furuse, a virtual girlfriend that interacts with you in an almost human-like manner. Ai Furuse is programmed with a dictionary of more than 30,000 words, and is able to respond to conversational cues. But more importantly, AI Furuse is able to learn from conversations. She can gather information about her interlocutor and, apparently, even identify changes in their mood. The more you converse with the chatbot, the more intimate and closer your relationship should be (in theory). Immediately, we can begin to see some big engineering questions. These are primarily about design, but remember - wherever you begin to think about design we’re starting to move towards the domain of ethics as well. The very process of learning through interaction requires the AI to be programmed in certain ways. It's a big challenge for engineers to determine what’s really important in these interactions. The need to make judgements on how users behave. The information that’s passed to the chatbot needs to be codified and presented in a way that can be understood and processed. That requires some work in itself. The models of desire on which Ai Furuse are necessarily limited. They bear the marks of the engineers that helped to create 'her'. It becomes a question of ethics once we start to ask if these models might be normative in some way. Do they limit or encourage certain ways of interacting? Desire algorithms In the context of one chatbot that might not seem like a big deal. But if (or as) the trend moves into the mainstream, we start to enter a world where the very fact of engineering chatbots inadvertently engineers the desires and sexualities that are expressed towards them. In this instance, not only do we shape the algorithms (which is what’s meant to happen), we also allow these ‘desire algorithms’ to shape our desires and wants too. Storing sexuality on the cloud But there’s another more practical issue as well. If the data on which sex chatbots or virtual lovers runs on the cloud, we’re in a situation where the most private aspects of our lives are stored somewhere that could easily be accessed by malicious actors. This a real risk of Ai Furuse, where cloud space is required for your ‘virtual girlfriend’ to ‘evolve’ further. You pay for additional cloud space. It’s not hard to see how this could become a problem in the future. Thousands of sexual and romantic conversations could be easily harvested for nefarious purposes. Sex robots, artificial intelligence and the problem of consent Language, then, is the kernel of sexualised artificial intelligence. Algorithms, when made well, should respond, process, adapt to and then stimulate further desire. But that's only half the picture. The physical reality of sex robots - both as literal objects, but also the physical effects of what they do - only adds a further complication into the mix. Questions about what desire is - why we have it, what we should do with it - are at the forefront of this debate. If, for example, a paedophile can use a child-like sex robot as a surrogate object of his desires, is that, in fact, an ethical use of artificial intelligence? Here the debate isn’t just about the algorithm, but how it should be deployed. Is the algorithm performing a therapeutic purpose, or is it actually encouraging a form of sexuality that fails to understand the concept of harm and consent? This is an important question in the context of sex robots, but it’s also an important question for the broader ethics of AI. If we can build an AI that is able to do something (ie. automate billions of jobs) should we do it? Who’s responsibility is it to deal with the consequences? The campaign against sex robots These are some of the considerations that inform the perspective of the Campaign Against Sex Robots. On their website, they write: "Over the last decades, an increasing effort from both academia and industry has gone into the development of sex robots – that is, machines in the form of women or children for use as sex objects, substitutes for human partners or prostituted persons. The Campaign Against Sex Robots highlights that these kinds of robots are potentially harmful and will contribute to inequalities in society. We believe that an organized approach against the development of sex robots is necessary in response the numerous articles and campaigns that now promote their development without critically examining their potentially detrimental effect on society." For the campaign, sex robots pose a risk in that they perpetuate already existing inequalities and forms of exploitation in society. They prevent us from facing up to these inequalities. They argue that it will “reduce human empathy that can only be developed by an experience of mutual relationship.” Consent and context Consent is the crucial problem when it comes to artificial intelligence. And you could say that it points to one of the limitations of artificial intelligence that we often miss - context. Algorithms can’t ever properly understand context. There will, undoubtedly be people who disagree with this. Algorithms can, for example, understand the context of certain words and sentences, right? Well yes, that may be true, but that’s not strictly understanding context. Artificial intelligence algorithms are set a context, one from which they cannot deviate. They can’t, for example, decide that actually encouraging a pedophile to act out their fantasies is wrong. It is programmed to do just that. But the problem isn’t simply with robot consent. There’s also an issue with how we consent to an algorithm in this scenario. As journalist Adam Rogers writes in this article for Wired, published at the start of 2018: "It’s hard to consent if you don’t know to whom or what you’re consenting. The corporation? The other people on the network? The programmer?" Rogers doesn’t go into detail on this insight, but it gets to the crux of the matter when discussing artificial intelligence and sex robots. If sex is typically built on a relationship between people, with established forms of communication that establish both consent and desire, what happens when this becomes literally codified? What happens when these additional layers of engineering and commerce get added on top of basic sexual interaction? Is the problem that we want artificial intelligence to be human? Towards the end of the same piece, Rogers finds a possible solutions from privacy researcher Sarah Jamie Lewis. Lewis wonders whether one of the main problems with sex robots is this need to think in humanoid terms. “We’re already in the realm of devices that look like alien tech. I looked at all the vibrators I own. They’re bright colors. None of them look like a penis that you’d associate with a human. They’re curves and soft shapes.” Of course, this isn’t an immediate solution - sex robots are meant to stimulate sex in its traditional (arguably heteronormative) sense. What Lewis suggests, and Rogers seems to agree with, is really just AI-assisted masturbation. But their insight is still useful. On reflection, there is a very real and urgent question about the way in which we deploy artificial intelligence. We need to think carefully about what we want it to replicate and what we want it to encourage. Sex robots are the starting point for thinking seriously about artificial intelligence It’s worth noting that when discussing algorithms we end up looping back onto ourselves. Sex robots, algorithms, artificial intelligence - they’re a problem insofar as they pose questions about what we really value as humans. They make us ask what we want to do with our time, and how we want to interact with other people. This is perhaps a way forward for anyone that builds or interacts with algorithms. Whether they help you get off, or find your next purchase. Consider what you’re algorithm is doing - what’s it encouraging, storing , processing, substituting. We can’t prepare for a future with artificial intelligence without seriously considering these things.

After more than five decades of incremental innovation, the manufacturing sector is finally ready to pivot thanks to Industry 4.0. Self-consciousness of technology plays a key role in ushering in Industry 4.0. AI in the manufacturing sector aims to achieve just that i.e. the creation of systems that can perceive their environment and take action consequently. One of the prominent minds in AI, Andrew Ng believes Factories to be AI's next frontier! Andrew is on a mission to AI-ify manufacturing with its new start-up called Landing.AI. For this initiative he partnered with Foxconn, world's largest contract manufacturer and makers of Apple iPhones. Together they aim to develop a wide range of AI transformation programs, from introduction of new technologies, operational processes, automated quality control and much more. In this AI-powered industrial revolution, machines are becoming smarter and interconnected. Manufacturers are using embedded intelligence of machines for collecting and analyzing data to generate meaningful insights. These are then used to run equipment efficiently, optimize workflows of operations and supply chains, among other things. Thus, AI is leaving an indelible impact across the manufacturing cycle. Further, a new wave of automation is transforming the role of human workforce wherein AI-driven robots are empowering production 24-hours a day. This is helping industrial environments gear up for the shift towards the smart factory environment. Below are some ways AI is revolutionizing manufacturing. Predictive analytics for increased production output Smart manufacturing systems are leveraging the power of predictive analysis and machine learning algorithms to enhance the production capacity. Predictive analytics derives its power from the data collected from the devices or sensors embedded in a manufacturer’s industrial equipments. These sensors become part of the IoT (Internet of Things) which collects and shares data with data scientists on the cloud. This setup is helping manufacturing industries to move from repair-and-replace to a predict-and-fix maintenance model. They do this by enabling these businesses to retrieve the right information at the right time to make the right decisions. For instance, in a pump manufacturing company, data scientists could collect, store and analyze sensor data based on machine attributes like heat, vibration, noise etc. This data can be stored in the cloud allowing for an array of analyses to be performed from understanding machine performance to predicting and monitoring disruption in processes and equipment remotely. Further, syncing up production schedules with parts availability can ensure enhanced production output. Enhancing product and service quality with machine and deep learning algorithms Manufacturers can deploy supervised/unsupervised ML, DL and reinforcement learning algorithms to monitor quality issues in the manufacturing process. For instance, researchers at Lappeenranta University of Technology in Finland have developed an innovative welding system for high-strength steel. They used unsupervised learning to allow the system to learn to mimic human’s ability to self-explore and self-correct. This welding system detects imperfections and self-corrects during the welding process using a new kind of sensor system controlled by a neural network program. Further, it also calculates other faults that may arise during the entire process. Visual inspection technology in an industrial environment identifies both functional and cosmetic defects. IBM has developed a new offering for manufacturing clients to automate visual quality inspections. Rooted in deep learning, a centralized ‘learning service’ collects images of all products - normal and abnormal. Next, it builds analytical models to recognize and classify different characteristics of machine parts and components into OK or NG. Characteristics that meet quality specifications are considered as OK while those that don't are classified as ‘NG’. Predictive maintenance for enhanced MRO (Maintenance, Repair and Overhaul) performance Manufacturing industries strive to achieve excellence throughout the production process. To ensure this, machinery embedded with sensors generate real-time performance and workload data. This helps in diagnosing faults and in predicting the need for equipment maintenance. For instance, a machine may break down due to lack of maintenance in the long run, incurring losses to business. With predictive maintenance, businesses can be better equipped to handle equipment malfunction by identifying significant causal factors like weather, temperature etc. Targeted predictive maintenance generates critical information such as which machine parts will need replacing and when. This helps in reducing equipment downtime, lowering maintenance costs and pre-emptively addressing aging equipment. Reinforcement Learning for managing warehouses Large warehouses face challenging times in streamlining space, managing inventories and reducing transit time. Manufacturing industries are employing reinforcement learning for efficient warehouse management. RL approach uses trial and error iterations within an environment to achieve a particular goal. Imagine what a breeze warehousing could be and the associated cost savings, if robots could pick up the right products from various lots and move them to the right destinations with great precision. Here, reinforcement learning based algorithms can improve the efficiency of such intelligent warehouses with multirobot systems by addressing task scheduling and path planning issues. Fanuc, a Tokyo based company, employs robots having reinforced learning ability to perform such tasks with great agility and precision. AI in supply chain management AI is helping manufacturers gain an in-depth understanding of the complex variables at play in the supply chain and in predicting future scenarios. To enable seamless insights generation, businesses are opting for more flexible and efficient cyber-physical systems. These intelligent systems are self-configurative and self-optimizing structures that can predict problems and minimize losses. Thus they help businesses to innovate rapidly by reducing the time to market, foresee uncertainties and deal with them promptly. Siemens, for example, is creating a self-organizing factory that aims to automate the entire supply chain by generating work orders using the demand and order information. Implication of AI in Industry 4.0 Industry 4.0 is the new way of manufacturing using automation, devices connected on the IoT, cloud and cognitive computing. It propagates the concept of the “smart factory” in which cyber-physical systems observe the physical processes of the factory and make discrete decisions accordingly. As AI finds its application in Industry 4.0, computers will merge together with robotics to automate and maximize the efficiency of the industrial processes. Powered by machine learning algorithms, the computer systems could control the robots with minimum human intervention. For instance, in a manufacturing setup, AI can work alongside systems like SCADA - to control industrial processes in an efficient manner. These systems can monitor, collect and process real-time data by directly interacting with devices such as sensors, pumps, motors etc. through human-machine interface (HMI) software. These machine-to-machine communication systems give new direction to the human-machine collaboration potential thus changing the way we see workforce management. Industry 4.0 will favor those who can build software, hardware, and firmware - those who can adapt and maintain new equipment and those who can design automation and robotics. Within Industry 4.0, augmented reality and virtual reality are other cutting edge production ready technologies that are making the idea of a smart factory a reality. The recent relaunch of Google Glass especially designed for the factory floor is worth a mention here. The Wi-Fi-enabled glasses allow factory workers, mechanics, and other technicians to view instructional videos, manuals, training videos etc., all in their line of sight. This helps in maintaining higher standards of work while ensuring safety with agility. In Conclusion Manufacturing industries are gearing themselves to harness AI along with IoT, AR/VR to create an agile manufacturing environment and to make smarter and real-time decisions. AI is helping realize the full potential of Industrial Internet of Things (IIoT) by applying machine learning, deep learning and other evolutionary algorithms to the sensor data. Human-machine collaboration is transforming the scenario at the fulfillment centers creating a win-win situation for both humans and robots. Robots employed at the fulfillment centers having motion sensors move on to the field of QR codes with precision and agility withouting running into each other creating a fascinating view. Imagine a real-life JARVIS from the movie Iron Man managing entire supply chains or factory spaces. The day is not far away when we can see a JARVIS like advanced virtual assistant uses sensors to collect real-time data, AI to process data,and blockchain to securely transmit the information while using AR to interact with us visually. It could take care of system and mechanical failures remotely while ceasing control of the factory for efficient energy management. Manufacturers could go save the world or unveil new products, Iron Man style!

Artificial intelligence might seem intimidating, but it isn’t actually as complex as you might think. Many of the tools that have been developed over the last decade or so have all helped to make artificial intelligence and machine learning more accessible to engineers with varying degrees of experience and knowledge. Today, we’ve got to a stage where it’s now accessible even to people who have barely written a line of code in their life! Pretty exciting, right? But if you’re completely new to the field, it can be challenging to know how to get started - fortunately, we’re about to help you overcome that first hurdle. If you are an AI denier, then be sure to first read ‘why learn Machine Learning as a non-techie’ before you move forward. A strong purpose and belief is the first step to learning anything new. Alright, now here’s how you can get started with artificial intelligence and machine learning techniques quickly. 0. Use a free MLaaS or a no code interactive machine learning tool to experience first hand what is possible with learning machine learning: Some popular examples of no code machine learning as a service option are Microsoft Azure, BigML, Orange, and Amazon ML. Read Q2 under the FAQ section below to know more on this topic. 1. Learn Linear Algebra: Linear Algebra is the elementary unit for ML. It helps you effectively comprehend the theory behind the Machine learning algorithms and how they work. It also improves your math skills such as statistics, programming skills, which are all other skills that helps in ML. Learning Resources: Linear Algebra for Beginners: Open Doors to Great Careers Linear algebra Basics 2. Learn just enough Python or any programming: Now, you can get started with any language of your interest, but we suggest Python as  it’s great for people who are new to programming. It’s easy to learn due to its simple syntax. You’ll be able to quickly implement the ML algorithms. Also,  It has a rich development ecosystem that offers a ton of libraries and frameworks in Machine Learning such as Scikit Learn, Lasagne, Numpy, Scipy, Theano, Tensorflow, etc. Learning Resources: Python Machine Learning Learn Python in 7 Days Python for Beginners 2017 [Video] Learn Python with codecademy Python editor for beginner programmers 3. Learn basic Probability Theory and statistics: A lot of fundamental Statistical and Probability Theories form the basis for ML. You’ve probably already learned Probability and statistics in school, it easy to dive into advanced statistics for ML. Machine learning in its currently widely used form is a way to predict odds and see patterns. Knowing statistics and probability is important as it will help you with better understanding of why any machine learning algorithm works. For example, your grounding in this area, will help to ask the right questions, choose the right set of algorithms and know what to expect as answers from your ML model on questions such as: What are the odds of this person also liking this movie given their current movie watching choices ( Collaborative filtering and content-based filtering) How similar is this user to that group of users who brought a bunch of stuff on my site (clustering, collaborative filtering, and classification) Could this person be at risk of cancer given a certain set of traits and health indicator observations (logistic regression) Should you buy that stock (decision tree) Also, check out our interview with James D. Miller to know more about why learning stats is important in this field. Learning resources: Statistics for Data Science [Video] 4. Learn machine learning algorithms: Do not get intimidated!  You don’t have to be an expert to learn ML algorithms. Knowing basic ML algorithms that are majorly used in the real world applications like linear regression, naive Bayes, and decision trees, are enough to get you started. Learn what they do and how they are used in Machine Learning. 5. Learn numpy sci-kit learn,Keras or any other popular machine learning framework: It can be confusing initially to decide which framework to learn. Each one has its own advantages and disadvantages. Numpy is a linear algebra library which is useful for performing mathematical and logical operations. You can easily work with large multidimensional arrays using Numpy. Sci-kit learn helps with quick implementation of popular algorithms on datasets as just one line of code makes different algorithms available for you. Keras is minimalistic and straightforward with high-levels of extensibility, so it is easier to approach. Learning Resources:  Hands-on Machine Learning with TensorFlow [Video]  Hands-on Scikit-learn for Machine Learning [Video] If you have reached till here, it is time to put your learning into practice. Go ahead and create a simple linear regression model using some publicly available dataset in your area of interest. Kaggle, ourworldindata.org, UC Irvine Machine Learning repository, elitedatascience, all have a rich set of clean datasets in varied fields. Now, it is necessary to commit and put in daily efforts to practise these skills. Quora, Reddit, Medium, and stackoverflow will be your best friends when it comes to solving doubts regarding any of these skills. Data Helpers is another great resource that provides newcomers with help on queries regarding entering the ML field and related topics. Additionally, once you start getting hang of these skills, identify your strengths and interests, to realign your career goals. Research on the kind of work you want to put your newly gained Machine Learning skill to use. It needn’t be professional or serious, it just needs to be something that you deeply care about or are passionate about. This will pull you through your learning milestones, should you feel low at some point. Also, don’t forget to collaborate with other people and learn from them. You can work with web developers, software programmers, data analysts, data administrators, game developers etc. Finally, keep yourself updated with all the latest happenings in the ML world. Follow top experts and influencers on social media, top blogs on Machine Learning, and conferences. Once you are done checking off these steps off your list, you’ll be ready to start off with your ML project.                                                  Now, we’ll be looking at the most frequently asked questions by beginners in the field of Machine learning. Frequently asked questions by Beginners in ML As a beginner, it’s natural to have a lot of questions regarding ML. We’ll be addressing the top three frequently asked questions by beginners or non-programmers when it comes to Machine learning: Q.1 I am looking to make a career in Machine learning but I have no prior programming experience. Do I need to know programming for Machine learning? In a nutshell, Yes. If you want a career in Machine learning then having some form of programming knowledge really helps. As mentioned earlier in this article, learning a programming language can really help you with implementing ML algorithms. It also lets you know the internal mechanism behind Machine learning. So, having programming as a prior skill is great. Again, as mentioned before, you can get started with Python which is the easiest and the most common languages for ML. However, programming is just a part of Machine learning. For instance, “machine learning engineers” typically write more code than develop models, while “research scientists” work more on modelling and analyzing different models. Now, ML is based on the principles of statistical inference and for talking statistically to the computer, we need a language, there comes Coding. So, even though the nature of your job in ML might not require you to code as much, there’s still some amount of coding required. Read Also: Why is Python so good for AI and ML? 5 Python Experts Explain Top languages for Artificial Intelligence development Q.2 Are there any tools that can help me with Machine learning without touching a single line of code? Yes. With the rise of MLaaS (Machine learning as a service), there are certain tools that help you get started with machine learning right-away. These are especially useful for business applications of ML, such as predictive modelling and clustering. Read Also: How MLaaS is transforming cloud Some of the most popular ones are: BigML:  This cloud based web-service lets you upload your data, prepare it and run algorithms on it. It’s great for people with not so extensive data science backgrounds. It offers a clean and easy to use interfaces for configuring algorithms (decision trees) and reviewing the results. Being focused “only” on Machine Learning, it comes with a wide set of features, all well integrated within a usable Web UI. Other than that, it also offers an API so that if you like it you can build an application around it. Microsoft Azure: The Microsoft Azure ML studio is a “GUI-based integrated development environment for constructing and operationalizing Machine Learning workflow on Azure”. So, via an integrated development environment called ML Studio, people without data science background or non-programmers can also build data models with the help of drag-and-drop gestures and simple data flow diagrams. This also saves a lot of time through ML Studio's library of sample experiments. Learning resources: Microsoft Azure Machine Learning Machine Learning In The Cloud With Azure ML[Video] Orange: This is an open source machine learning and data visualization studio for novice and experts alike. It provides a toolbox comprising of text mining (topic modelling) and image recognition. It also offers a design tool for visual programming which allows you to connect together data preparation, algorithms, and result evaluation, thereby, creating machine learning “programs”. Apart from that, it provides over 100 widgets for the environment and there’s also a Python API and library available which you can integrate into your application. Amazon ML: Amazon ML is a part of Amazon Web Services ( AWS ) that combines powerful machine learning algorithms with interactive visual tools to guide you towards easily creating, evaluating, and deploying machine learning models. So, whether you are a data scientist or a newbie, it offers ML services and tools tailored to meet your needs and level of expertise. Building ML models using Amazon ML consists of three operations: data analysis, model training, and evaluation. Learning Resources: Effective Amazon Machine Learning Q.3  Do I need to know advanced mathematics ( college graduate level ) to learn Machine learning? It depends. As mentioned earlier, understanding of the following mathematical topics: Probability, Statistics and Linear Algebra can really make your machine learning journey easier and also help simplify your code. These help you understand the “why” behind the working of the machine learning algorithms, which is quite fundamental to understanding ML. However, not knowing advanced mathematics is not an excuse to not learning Machine Learning. There a lot of libraries which makes the task of applying an ML algorithm to solve a task easier. One such example is the widely used Python’s scikit-learn library. With scikit-learn, you just need one line of code and you’ll have the most common algorithms there for you, ready to be used. But, if you want to go deeper into machine learning then knowing advanced mathematics is a prerequisite as it will help you understand the algorithms, the formulas, how the learning is done and many other Machine Learning concepts. Also, with so many courses and tutorials online, you can always learn advanced mathematics on the side while exploring Machine learning. So, we looked at the three most asked questions by beginners in the field of Machine Learning. In the past, machine learning has provided us with self-driving cars, effective web search, speech recognition, etc. Machine learning is extremely pervasive, in fact, many researchers believe that ML is the best way to make progress towards human-level AI. Learning ML is not an easy task but its not next to impossible either. In the end, it all depends on the amount of dedication and efforts that you’re willing to put in to get a grasp of it. We just touched the tip of the iceberg in this article, there’s a lot more to know in Machine Learning which you will get a hang of as you get your feet dirty in it. That being said, all the best for the road ahead! Facebook launches a 6-part ML video series 7 of the best ML conferences for the rest of 2018 Google introduces Machine Learning courses for AI beginners

At the wee hours of the night, on January 4 this year, over 9.8 million people experienced a magnitude 4.4 earthquake that rumbled across the San Francisco Bay Area. This was followed by a magnitude 7.6 earthquake in the Caribbean sea on January 9, following which a tsunami advisory was in effect for Puerto Rico and the U.S. and British Virgin Islands. In the past 6 months, the United States alone has witnessed four back-to-back storms from one brutal hurricane season, and a massive wildfire with almost 2 million acres of land ablaze. Natural Disasters across the globe are increasingly becoming more damaging and frequent. Since 1970, the number of disasters worldwide has more than quadrupled to around 400 a year. These series of natural disasters have strained the emergency services and disaster relief operations beyond capacity. We now need to look for newer ways to assist the affected people and automate the recovery process. Artificial intelligence and machine learning have advanced to the state where they are highly proficient in making predictions, and in identification and classification tasks. These use cases of AI can also be applied to prevent disasters or respond quickly, in case of an emergency.   Here are 5 ways how AI can lend a helping hand during emergency situations. 1. Machine Learning for targeted disaster relief management In case of any disaster, the first step is to formulate a critical response team to help those in distress. Before the team goes into action, it is important to analyze and assess the extent of damage and to ensure that the right aid goes first to those who need it the most. AI techniques such as image recognition and classification can be quite helpful in assessing the damage as they can analyze and observe images from the satellites. They can immediately and efficiently filter these images, which would have required months to be sorted manually. AI can identify objects and features such as damaged buildings, flooding, blocked roads from these images. They can also identify temporary settlements which may indicate that people are homeless, and so the first care could be directed towards them.   Artificial intelligence and machine learning tools can also aggregate and crunch data from multiple resources such as crowd-sourced mapping materials or Google maps. Machine learning approaches then combine all this data together, remove unreliable data, and identify informative sources to generate heat maps. These heat maps can identify areas in need of urgent assistance and direct relief efforts to those areas. Heat maps are also helpful for government and other humanitarian agencies in deciding where to conduct aerial assessments. DigitalGlobe provides space imagery and geospatial content. Their Open Data Program is a special program for disaster response. The software learns how to recognize buildings on satellite photos by learning from the crowd. DigitalGlobe releases pre- and post-event imagery for select natural disasters each year, and their crowdsourcing platform, Tomnod, will prioritize micro-tasking to accelerate damage assessments. Following the Nepal Earthquakes in 2015, Rescue Global and academicians from the Orchid Project used machine learning to carry out rescue activities. They took pre and post-disaster imagery and utilized crowd-sourced data analysis and machine learning to identify locations affected by the quakes that had not yet been assessed or received aid. This information was then shared with relief workforces to facilitate their activities. 2. Next Generation 911 911 is the first source of contact during any emergency situation. 911 dispatch centers are already overloaded with calls on a regular day. In case of a disaster or calamity, the number gets quadrupled, or even more. This calls for augmenting traditional 911 emergency centers with newer technologies for better management. Traditional 911 centers rely on voice-based calls alone. Next-gen dispatch services are upgrading their emergency dispatch technology with machine learning to receive more types of data. So now they can ingest the data from not just calls but also from text, video, audio, and pictures, to analyze them to make quick assessments. The insights gained from all this information can be passed on to the emergency response teams out in the field to efficiently carry out critical tasks. The Association of Public-Safety Communications (APCO) have employed IBM’s Watson to listen to 911 calls. This initiative is to help emergency call centers improve operations and public safety by using Watson’s speech-to-text and analytics programs. Using Watson's speech-to-text function, the context of each call is fed into the AI's analytics program allowing improvements in how call centers respond to emergencies. It also helps in reducing call times, provide accurate information, and help accelerate time-sensitive emergency services. 3. Sentiment analysis on social media data for disaster management and recovery Social media channels are a major source of news in present times. Some of the most actionable information, during a disaster, comes from social media users. Real-time images and comments from Facebook, Twitter, Instagram, and YouTube can be analyzed and validated by AI to filter real information from fake ones. These vital stats can help on-the-ground aid workers to reach the point of crisis sooner and direct their efforts to the needy. This data can also help rescue workers in reducing the time needed to find victims. In addition, AI and predictive analytics software can analyze digital content from Twitter, Facebook, and Youtube to provide early warnings, ground-level location data, and real-time report verification. In fact, AI could also be used to view the unstructured data and background of pictures and videos posted to social channels and compare them to find missing people. AI-powered chatbots can help residents affected by a calamity. The chatbot can interact with the victim, or other citizens in the vicinity via popular social media channels and ask them to upload information such as location, a photo, and some description. The AI can then validate and check this information from other sources and pass on the relevant details to the disaster relief committee. This type of information can assist them with assessing damage in real time and help prioritize response efforts. AI for Digital Response (AIDR) is a free and open platform which uses machine intelligence to automatically filter and classify social media messages related to emergencies, disasters, and humanitarian crises. For this, it uses a Collector and a Tagger. The Collector helps in collecting and filtering tweets using keywords and hashtags such as "cyclone" and "#Irma," for example. The Collector works as a word-filter. The Tagger is a topic-filter which classifies tweets by topics of interest, such as "Infrastructure Damage," and "Donations," for example. The Tagger automatically applies the classifier to incoming tweets collected in real-time using the Collector. 4. AI answers distress and help-calls Emergency relief services are flooded with distress and help calls in the event of any emergency situation. Managing such a huge amount of calls is time-consuming and expensive when done manually. The chances of a critical information being lost or unobserved is also a possibility. In such cases, AI can work as a 24/7 dispatcher. AI systems and voice assistants can analyze massive amounts of calls, determine what type of incident occurred and verify the location. They can not only interact with callers naturally and process those calls, but can also instantly transcribe and translate languages. AI systems can analyze the tone of voice for urgency, filtering redundant or less urgent calls and prioritizing them based on the emergency. Blueworx is a powerful IVR platform which uses AI to replace call center officials. Using AI technology is especially useful when unexpected events such as natural disasters drive up call volume. Their AI engine is well suited to respond to emergency calls as unlike a call center agent, it can know who a customer is even before they call. It also provides intelligent call routing, proactive outbound notifications, unified messaging, and Interactive Voice Response. 5. Predictive analytics for proactive disaster management Machine learning and other data science approaches are not limited to assisting the on-ground relief teams or assisting only after the actual emergency. Machine learning approaches such as predictive analytics can also analyze past events to identify and extract patterns and populations vulnerable to natural calamities. A large number of supervised and unsupervised learning approaches are used to identify at-risk areas and improve predictions of future events. For instance, clustering algorithms can classify disaster data on the basis of severity. They can identify and segregate climatic patterns which may cause local storms with the cloud conditions which may lead to a widespread cyclone. Predictive machine learning models can also help officials distribute supplies to where people are going, rather than where they were by analyzing real-time behavior and movement of people. In addition, predictive analytics techniques can also provide insight for understanding the economic and human impact of natural calamities. Artificial neural networks take in information such as region, country, and natural disaster type to predict the potential monetary impact of natural disasters. Recent advances in cloud technologies and numerous open source tools have enabled predictive analytics with almost no initial infrastructure investment. So agencies with limited resources can also build systems based on data science and develop more sophisticated models to analyze disasters.   Optima Predict, a suite of software by Intermedix collects and reads information about disasters such as viral outbreaks or criminal activity in real time. The software spots geographical clusters of reported incidents before humans notice the trend and then alerts key officials about it. The data can also be synced with FirstWatch, which is an online dashboard for an EMS (Emergency Medical Services) personnel. Thanks to the multiple benefits of AI, government agencies and NGOs can start utilizing machine learning to deal with disasters. As AI and allied fields like robotics further develop and expand, we may see a fleet of drone services, equipped with sophisticated machine learning. These advanced drones could expedite access to real-time information at disaster sites using video capturing capabilities and also deliver lightweight physical goods to hard to reach areas. As with every progressing technology, AI will also build on its existing capabilities. It has the potential to eliminate outages before they are detected and give disaster response leaders an informed, clearer picture of the disaster area, ultimately saving lives.

Now that you’ve opened this article, I’ll assume you’re a web developer who is all excited with the prospect of building a machine learning project. You may be here for one of these reasons. Either you have been in a circle of people who find web development is dying? (Is it really dying or just unwell?). Or maybe you are stagnating in your current trajectory. And so, you want to learn something different, something trending, something like Artificial Intelligence. Or you/your employer/your client is aware of the capabilities of machine learning and want to include it in some part of your web app to make it more powerful. Or like the majority of the folks, you just want to see first hand if all the fuss about artificial intelligence is really worth all the effort to switch gears, by building a side toy ML project. Either way, there are different approaches to fulfill these needs. Learning Machine Learning for the Web with Javascript Learning machine learning coming from a web development background comes with its own constraints. You might worry about having to learn entirely different concepts from scratch - from different algorithms to programming languages like Python to mathematical concepts like linear algebra, calculus, and statistics. However, chances are you can skip learning a new language. You probably know some Javascript in some form or the other thanks to your web development experience. As such, you can learn Machine Learning in JavaScript (You don’t have to learn another programming language from scratch) and take it right to your browsers with WebGL. There are some advantages to using JavaScript for ML. Its popularity is one; while ML in JavaScript is not as popular as Python’s ML ecosystem, at the moment, the language itself is. As demand for ML applications rises, and as hardware becomes faster and cheaper, it's only natural for machine learning to become more prevalent in the JavaScript world. The JavaScript ecosystem offers a rich set of libraries suited for most Machine Learning tasks. Math: math.js Data Analysis: d3.js Server: node.js (express, koa, hapi) Performance: Tensorflow.js (e.g. GPU accelerated via WebGL API in the browser), Keras.js etc. Read also: 5 JavaScript machine learning libraries you need to know BRIIM is a good collection of materials to get you started as web developer or JavaScript enthusiast in machine learning. In case you’re interested in learning Python instead of Javascript, here are the set of libraries you should pick. Math: numpy Data Analysis: Pandas Data Mining: PySpark Server: Flask, Django Performance: TensorFlow (because it is written with a Python API over a C/C++ engine) or Keras (sits on top of TensorFlow). Using Machine Learning as a service If you don’t want to spend your time learning frameworks, tools, and languages suited for machine learning, you can adopt Machine Learning as a service or MLaaS. These services provide machine learning tools as part of cloud computing services. So basically, you can benefit from machine learning without the allied cost, time and risk of establishing an in-house internal machine learning team. All you need is sufficient knowledge of incorporating APIs. All Machine Learning tasks including data pre-processing, model training, model evaluation, and predictions can be completed through MLaaS. Read also: How machine learning as a service is transforming cloud A large number of companies provide Machine Learning as a service. Most prominent ones include: Amazon Machine Learning Amazon ML makes it easy for web developers to build smart applications using simple APIs. This includes applications for fraud detection, demand forecasting, targeted marketing, and click prediction. They provide a Developer Guide, which provides a conceptual overview of Amazon ML and includes detailed instructions for using the service. They also have a API reference, which describes all the API operations and provides sample requests and responses for supported web service protocols. Azure ML web app templates The web app templates available in the Azure Marketplace can build a custom web app that knows your web service's input data and expected results. All you need to do is give the web app access to your web service and data, and the template does the rest. There are two available templates: Azure ML Request-Response Service Web App Template Azure ML Batch Execution Service Web App Template Each template creates a sample ASP.NET application by using the API URI and key for your web service. The template then deploys the application as a website to Azure. No coding is required to use these templates. You just supply the API key and URI, and the template builds the application for you. Google Cloud based APIs Google also provides machine learning services, with pre-trained models and a service to generate your own tailored models. Google’s Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. Cloud AutoML is used by Disney on their website shopDisney to enhance guest experience through more relevant search results, expedited discovery, and product recommendations. Building Conversational Interfaces As a web developer, another thing you might be looking into, is developing conversational interfaces or chatbots to enhance your web apps. Amazon, Google, and Microsoft provide Machine learning powered tools to help developers with building their own chatbots. Amazon Lex You can embed chatbots in your web apps with the Amazon Lex featuring ASR (Automatic Speech Recognition) and NLP (Natural Language Processing) capabilities. The API can recognize written and spoken text and the Lex interface allows you to hook the recognized inputs to various back-end solutions. Lex currently supports deploying chatbots for Facebook Messenger, Slack, and Twilio. Google Dialogflow Google’s Dialogflow can build voice and text-based conversational interfaces, such as voice apps and chatbots, powered by AI. Dialogflow incorporates Google's machine learning expertise and products such as Google Cloud Speech-to-Text. The API can be tweaked and customized for needed intents using Java, Node.js, and Python. It is also available as an enterprise edition. Microsoft Azure Cognitive Services Microsoft Cognitive Services simplify a variety of AI-based tasks, giving you a quick way to add intelligence technologies to your bots with just a few lines of code. It provides tools and APIs for aiding the development of conversational interfaces. These include: Translator Speech API Bing Speech API to convert text into speech and speech into text Speaker Recognition API for voice verification tasks Custom Speech Service to apply Azure NLP capacities using own data and models Language Understanding Intelligent Service (LUIS) is an API that analyzes intentions in text to be recognized as commands Text Analysis API for sentiment analysis and defining topics Bing Spell Check Translator Text API Web Language Model API that estimates probabilities of words combinations and supports word autocompletion Linguistic Analysis API used for sentence separation, tagging the parts of speech, and dividing texts into labeled phrases Read also: Top 4 chatbot development frameworks for developers These tools should be enough to get your feet off the ground quickly and move into the specific area of machine learning. Ultimately your choice of tool relies on the kind of application you want to build, your level of expertise, and how much time and effort you’re willing to put to learn. Obviously, depending on your area of choice, you would have to do more research and develop yourself in those areas. How should web developers learn machine learning? 5 examples of Artificial Intelligence in Web apps The most valuable skills for web developers to learn in 2018

All around us, our perception of learning and intellect is being challenged daily with the advent of new and emerging technologies. From self-driving cars, playing Go and Chess, to computers being able to beat humans at classic Atari games, the advent of a group of technologies we colloquially call Machine Learning have come to dominate a new era in technological growth – a new era of growth that has been compared with the same importance as the discovery of electricity and has already been categorized as the next human technological age. Games and simulations are no stranger to AI technologies and there are numerous assets available to the Unity developer in order to provide simulated machine intelligence. These technologies include content like Behavior Trees, Finite State Machine, navigation meshes, A*, and other heuristic ways game developers use to simulate intelligence. So, why Machine Learning and why now? The reason is due in large part to the OpenAI initiative, an initiative that encourages research across academia and the industry to share ideas and research on AI and ML. This has resulted in an explosion of growth in new ideas, methods, and areas for research. This means for games and simulations that we no longer have to fake or simulate intelligence. Now, we can build agents that learn from their environment and even learn to beat their human builders. This article is an excerpt taken from the book 'Learn Unity ML-Agents – Fundamentals of Unity Machine Learning'  by Micheal Lanham. In this article, we look at the role that machine learning plays in game development. Machine Learning is an implementation of Artificial Intelligence. It is a way for a computer to assimilate data or state and provide a learned solution or response. We often think of AI now as a broader term to reflect a "smart" system. A full game AI system, for instance, may incorporate ML tools combined with more classic AIs like Behavior Trees in order to simulate a richer, more unpredictable AI. We will use AI to describe a system and ML to describe the implementation. How Machine Learning is useful in gaming Game engines have embraced the idea of incorporating ML into all aspects of its product and not just for use as a game AI. While most developers may try to use ML for gaming, it certainly helps game development in the following areas: Map/Level Generation: There are already plenty of examples where developers have used ML to auto-generate everything from dungeons to the realistic terrain. Getting this right can provide a game with endless replayability, but it can be some of the most challenging ML to develop. Texture/Shader Generation: Another area that is getting the attention of ML is texture and shader generation. These technologies are getting a boost brought on by the attention of advanced generative adversarial networks, or GAN. There are plenty of great and fun examples of this tech in action; just do a search for DEEP FAKES in your favorite search engine. Model Generation: There are a few projects coming to fruition in this area that could greatly simplify 3D object construction through enhanced scanning and/or auto-generation. Imagine being able to textually describe a simple model and having ML build it for you, in real-time, in a game or other AR/VR/MR app, for example. Audio Generation: Being able to generate audio sound effects or music on the fly is already being worked on for other areas, not just games. Yet, just imagine being able to have a custom designed soundtrack for your game developed by ML. Artificial Players: This encompasses many uses from the gamer themselves using ML to play the game on their behalf to the developer using artificial players as enhanced test agents or as a way to engage players during low activity. If your game is simple enough, this could also be a way of auto testing levels. NPCs or Game AI: Currently, there are better patterns out there to model basic behavioral intelligence in the form of Behavior Trees. While it's unlikely that BTs or other similar patterns will go away any time soon, imagine being able to model an NPC that may actually do an unpredictable, but rather cool behavior. This opens all sorts of possibilities that excite not only developers but players as well. So, we learned about different areas of the gaming world such as model generation, artificial players, NPCs, level generation, etc, where Machine learning can be extensively used. If you found this post useful, be sure to check out the book 'Learn Unity ML-Agents – Fundamentals of Unity Machine Learning' to learn more machine learning concepts in gaming. 5 Ways Artificial Intelligence is Transforming the Gaming Industry How should web developers learn machine learning? Deep Learning in games – Neural Networks set to design virtual worlds